Sensen Wu

Orcid: 0000-0001-9322-0149

According to our database1, Sensen Wu authored at least 21 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
GSA-SiamNet: A Siamese Network with Gradient-Based Spatial Attention for Pan-Sharpening of Multi-Spectral Images.
Remote. Sens., February, 2024

Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations.
ISPRS Int. J. Geo Inf., January, 2024

Single Remote Sensing Image Super-Resolution via a Generative Adversarial Network With Stratified Dense Sampling and Chain Training.
IEEE Trans. Geosci. Remote. Sens., 2024

DOCNet: Dual-Domain Optimized Class-Aware Network for Remote Sensing Image Segmentation.
IEEE Geosci. Remote. Sens. Lett., 2024

2023
High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing-Tianjin-Hebei Region Based on TROPOMI Data.
Remote. Sens., August, 2023

A Graph Memory Neural Network for Sea Surface Temperature Prediction.
Remote. Sens., July, 2023

A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression.
Remote. Sens., April, 2023

2022
A deep trajectory clustering method based on sequence-to-sequence autoencoder model.
Trans. GIS, 2022

Effects of Climate Change on Corn Yields: Spatiotemporal Evidence from Geographically and Temporally Weighted Regression Model.
ISPRS Int. J. Geo Inf., 2022

House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China.
ISPRS Int. J. Geo Inf., 2022

Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid.
Int. J. Geogr. Inf. Sci., 2022

House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China.
CoRR, 2022

A deep learning crop model for adaptive yield estimation in large areas.
Int. J. Appl. Earth Obs. Geoinformation, 2022

Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model.
Int. J. Appl. Earth Obs. Geoinformation, 2022

2021
Satellite-Based Mapping of High-Resolution Ground-Level PM2.5 with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression.
Remote. Sens., 2021

A GloVe-Based POI Type Embedding Model for Extracting and Identifying Urban Functional Regions.
ISPRS Int. J. Geo Inf., 2021

Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland.
ISPRS Int. J. Geo Inf., 2021

Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships.
Int. J. Geogr. Inf. Sci., 2021

2020
Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity.
Int. J. Geogr. Inf. Sci., 2020

2018
A spatiotemporal regression-kriging model for space-time interpolation: a case study of chlorophyll-a prediction in the coastal areas of Zhejiang, China.
Int. J. Geogr. Inf. Sci., 2018

Extending geographically and temporally weighted regression to account for both spatiotemporal heterogeneity and seasonal variations in coastal seas.
Ecol. Informatics, 2018


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